In situ monitoring of stress for additively manufactured components

ABSTRACT

A material deposition process including in situ sensor analysis of a component in a formation state is provided. The material deposition process is implemented in part by a sensor device of an additive manufacturing machine producing the component. The material deposition process includes sensing, by the sensing device, in situ physical properties of an area of interest of the component during a three-dimensional object production. Compliance to specifications or defects are then detected in the in situ physical properties with respect to pre-specified material requirements. The defects are analyzed to determine corrective actions, and an updated three-dimensional object production, which includes the corrective actions, is implemented to complete the component.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a division of U.S. application Ser. No. 16/166,931filed Oct. 22, 2018, issued as U.S. Pat. No. 11,292,198, Issued Apr. 5,2022, the disclosure of which is incorporated herein by reference in itsentirety.

BACKGROUND

Conventional additive manufacturing processes have limited or no closedloop controls and, therefore, rely on final material propertyassessments of a finished manufactured part or product. Specifically,conventional additive manufacturing utilizes post deposition analysis toprovide these assessments.

BRIEF DESCRIPTION

In accordance with one or more embodiments, a material depositionprocess including in situ sensor analysis of a component in a formationstate is provided. The material deposition process is implemented inpart by an X-ray source and an X-ray detector of an additivemanufacturing machine producing the component. The material depositionprocess includes sensing, by the X-ray source and the X-ray detector, insitu physical properties of an area of interest of the component duringa three-dimensional object production. Compliance to specifications ordefects are then detected in the in situ physical properties withrespect to pre-specified material requirements. The defects are analyzedto determine corrective actions, and an updated three-dimensional objectproduction, which includes the corrective actions, is implemented tocomplete the component.

In accordance with one or more embodiments or the material depositionprocess embodiment above, the material deposition process can includeimplementing the three-dimensional object production of the componentaccording to a computer design file.

In accordance with one or more embodiments or any of the materialdeposition process embodiments above, the material deposition processcan include feeding forward and back the corrective actions to thethree-dimensional object production in real time to generate the updatedthree-dimensional object production.

In accordance with one or more embodiments or any of the materialdeposition process embodiments above, the at least one sensing devicecan include an X-ray source and X-ray detector that together acquire afull or partial X-ray diffraction signal or pattern that is analyzed todetermine the in situ physical properties.

In accordance with one or more embodiments or any of the materialdeposition process embodiments above, the in situ physical propertiescan potentially include: hardness, local strain, yield strength,density, crystallite size, porosity, defect density, crystallineorientation, texture, and compositional variation.

In accordance with one or more embodiments or any of the materialdeposition process embodiments above, a compute device can include aprocessor executing software to provide one or more process modeling,toolpath planning, defect detection, layer defect detection, part defectdetection, feedback control, scan path planning, decision making, andprocess sensing operations for detecting the defects.

In accordance with one or more embodiments or any of the materialdeposition process embodiments above, a compute device can include adatabase storing and providing the pre-specified material requirementsand a computer design file for detecting the defects and implementingthe three-dimensional object production.

In accordance with one or more embodiments, a system for implementing athree-dimensional object production of a component via an additivemanufacturing is provided. The system includes an additive manufacturingmachine including an X-ray source and an X-ray detector. The system alsoincludes a compute device including a processor and a memory. Thecompute device is communicatively coupled to the additive manufacturingmachine and the X-ray source and the X-ray detector. The additivemanufacturing machine and the compute device provide in situ sensoranalysis of the component while in a formation state during a materialdeposition process of the additive manufacturing by sensing, by theX-ray source and the X-ray detector, in situ physical properties of anarea of interest of the component during a three-dimensional objectproduction. Compliance to specifications or defects are then detected inthe in situ physical properties with respect to pre-specified materialrequirements. The defects are analyzed to determine corrective actions,and an updated three-dimensional object production, which includes thecorrective actions, is implemented to complete the component.

In accordance with one or more embodiments or the system embodimentabove, the three-dimensional object production of the component can beimplemented according to a computer design file.

In accordance with one or more embodiments or any of the systemembodiments above, the compute device can feed forward and back thecorrective actions to the three-dimensional object production in realtime to generate the updated three-dimensional object production.

In accordance with one or more embodiments or any of the systemembodiments above, the at least one sensing device can include an X-raysource and X-ray detector that together acquire a full or partial X-raydiffraction signal or pattern that is analyzed to determine the in situphysical properties.

In accordance with one or more embodiments or any of the systemembodiments above, the in situ physical properties can include hardness,local strain, yield strength, density, crystallite size, porosity,defect density and compositional variation.

In accordance with one or more embodiments or any of the systemembodiments above, a compute device can include a processor executingsoftware to provide one or more process modeling, toolpath planning,defect detection, layer defect detection, part defect detection,feedback control, scan path planning, decision making, and processsensing operations for detecting the defects.

In accordance with one or more embodiments or any of the systemembodiments above, a compute device can include a database storing andproviding the pre-specified material requirements and a computer designfile for detecting the defects and implementing the three-dimensionalobject production.

BRIEF DESCRIPTION OF THE DRAWINGS

The following descriptions should not be considered limiting in any way.With reference to the accompanying drawings, like elements are numberedalike:

FIG. 1 depicts a system according to one or more embodiments;

FIG. 2 depicts a process flow according to one or more embodiments; and

FIG. 3 depicts a schematic flow according to one or more embodiments.

DETAILED DESCRIPTION

A detailed description of one or more embodiments of the disclosedapparatus and method are presented herein by way of exemplification andnot limitation with reference to the Figures.

Turning now to an overview of technologies that are more specificallyrelevant to aspects of the invention, as discussed above, conventionaladditive manufacturing is rapidly emerging means of flexiblemanufacturing. However, part-to-part variation, non-uniformity ofproperties across finished manufactured parts or products, and local orextended defects are significant concerns in utilizing conventionaladditive manufacturing for high volume production. Most conventionaladditive manufacturing processes have limited or no closed loop control.Therefore, post deposition analysis is employed to assess only finalmaterial properties of the finished manufactured part or productrelative to pre-determined materials requirements. Further, postdeposition analysis does not allow a manufacturer to change or adaptproperties during manufacturing.

Turning now to an overview of the aspects of the invention, one or moreembodiments of the invention address the above-described shortcomings ofthe conventional additive manufacturing by providing, via a system, amethod, and/or an apparatus (referred to as a system, herein, forbrevity), material deposition processes including in situ sensoranalysis. The in situ sensor analysis of the material depositionprocesses extracts physical properties of a component in a formationstate during its additive manufacturing. The material depositionprocesses, then, feed forward and back these physical properties to theadditive manufacturing for continuous adaptability. The technicaleffects and benefits of embodiments of the material deposition processesherein include determining these physical properties during theformation state of the component and, thus, enabling corrective actions,such as altering additive manufacturing depositions, to achievepre-specified material requirements.

Turning now to FIG. 1, a system 100 for implementing the teachingsherein is shown in according to one or more embodiments. The system 100implements material deposition processes including in situ sensoranalysis.

In this embodiment, the system 100 includes a compute device 101. Thecompute device 101 can be an electronic, computer framework comprisingand/or employing any number and combination of computing device andnetworks utilizing various communication technologies, as describedherein. The compute device 101 can be easily scalable, extensible, andmodular, with the ability to change to different services or reconfiguresome features independently of others.

The compute device 101 has a processor 102, which can include one ormore central processing units (CPUs). The processor 102, also referredto as a processing circuit, microprocessor, computing unit, is coupledvia a system bus 103 to a system memory 104 and various othercomponents. The system memory 104 includes read only memory (ROM) andrandom access memory (RAM). The ROM is coupled to the system bus 103 andmay include a basic input/output system (BIOS), which controls certainbasic functions of the system 100. The RAM is read-write memory coupledto the system bus 103 for use by the processor 102.

The compute device 101 includes a hard disk 107, which is an example ofa tangible storage medium readable executable by the processor 102. Thehard disk 107 stores software 108 and database 109. The software 108 isstored as instructions for execution on the system 100 by the processor102 (to perform process, such as the process flows of FIGS. 2-3). Thedatabase 109 includes a set of values of qualitative or quantitativevariables organized in various data structures to support and be used byoperations of the software 108. Examples of operations provided by thesoftware 108 include process modeling, toolpath planning, defectdetection, layer defect detection, part defect detection, feedbackcontrol, scan path planning, decision making, and process sensing.Examples of items stored on the database 109 include computer designfiles, pre-specified material requirements, assessment models,assessment algorithms, and the like.

The compute device 101 includes one or more adapters (e.g., hard diskcontrollers, network adapters, graphics adapters, etc.) thatinterconnect and support communications between the processor 102, thesystem memory 104, the hard disk 107, and other components of thetranslation system 100 (e.g., peripheral and external devices). In oneor more embodiments of the present invention, the one or more adapterscan be connected to one or more I/O buses that are connected to thesystem bus 103 via an intermediate bus bridge, and the one or more I/Obuses can utilize common protocols, such as the Peripheral ComponentInterconnect (PCI).

The compute device 101 includes an interface adapter 110 interconnectinga keyboard, a mouse, a speaker, a microphone, etc. to the system bus103. The compute device 101 includes a display adapter 111interconnecting the system bus 103 to a display. The display adapter 111(and/or the processor 102) can include a graphics controller to providegraphics performance, such as a display and management of a graphic userinterface. A communications adapter 113 interconnects the system bus 103with a network 120 enabling the translation system 100 to communicatewith other systems, devices, data, and software, such as an additivemanufacturing machine 130.

The system 100 includes the additive manufacturing machine 130, whichfurther comprises at least one sensor device 131, along with aprocessor, a memory, tool/feeder, and other machining parts that are notshown for brevity. Note that while shown as separate mechanismscommunicating across the network 120, in accordance with one or moreembodiment, the compute device 101 and the additive manufacturingmachine 130 can be integrated into a single apparatus.

The additive manufacturing machine 130 is configured to manufacture acomponent 140 via the material deposition processes including in situsensor analysis. In general, additive manufacturing is athree-dimensional object production process utilizing computer designfile. In this regard, a variety of materials, ranging from polymercomposites, metals, ceramics, food, foams, gels, alloys, and the like,are deposited by a tool or feeder according to the computer design fileand heated by an electric beam to set the material in place. Thelocation of the deposited materials as the tool or feeder movesaccording to the computer design file is referred to as a tool path.

The at least one sensor device 131 can be any device includingtransducer and/or a generator. In general, the transducer of the sensordevice 131 can be any detector converts variations in a physicalquantity into an electrical signal. Examples of physical quantities caninclude such as local strain, yield strength, density, crystallite size,porosity, defect density, crystalline orientation, texture,compositional variation, temperature, local porosity, optical density,reflectance (e.g., note that because some of these quantities aredifficult to extract, the sensor device 131 provides added benefits forin situ analysis). The generator (also known as a source) of the sensordevice 131 can be any mechanism that, in response to electrical signals,generates a wave, which itself is detectable or a reflection thereof isdetectable by the transducer. The at least one sensor device 131 canalso communicate via any interface, such as a controller area network(CAN), a local interconnect network (LIN), a direct I/O interface, ananalog to digital (A/D) interface, a digital to analog (D/A) interface,or any other interface specific to the input, to the compute device 101via the network 130, along with a processor, a memory, and machiningparts of the additive manufacturing machine 130. Note that the at leastone sensor device 131 is representative of one or more sensors of thesame or varying type, each of which is capable of extracting physicalproperties of the component 140 in a formation state during its additivemanufacturing. Example of the at least one sensor device 131 include,but are not limited to, an X-ray, ultra-violet, visible light,near-infrared, short-wave infrared, mid-wavelength infrared,long-wavelength infrared, and terahertz sensors, cameras, and detectors.In accordance with one or more embodiments, the at least one sensordevice 131 includes an X-ray source and X-ray detector that togetheracquire a full or partial X-ray diffraction signal or pattern that isanalyzed to determine the in situ physical properties. Further, theX-ray source and the X-ray detector can be directed to detect a smallportion of the full X-ray diffraction pattern, such that a single peakwith a particular intensity and width representing the detection.

Thus, as configured in FIG. 1, the operations of the software 108, thedatabase 109, and the additive manufacturing machine 130 (e.g., thesystem 100) are necessarily rooted in the computational ability of theprocessors therein to overcome and address the herein-describedshortcomings of the conventional additive manufacturing. In this regard,the software 108 and the data 109 improve manufacturing operations ofthe additive manufacturing machine 130 by reducing and eliminatingerrors in manufacturing, part-to-part variation, non-uniformity ofproperties, and local or extended defects for high volume production.

FIG. 2 depicts a process flow 200 of according to one or moreembodiments. The process flow 200 is an example operation ofimplementing material deposition processes including in situ sensoranalysis of the component 140 in a formation state during its additivemanufacturing by the system 100.

The process flow 200 being at block 210, where the system 100 implementsa material deposition process to form the component 140 according to acomputer design file. In this regard, the additive manufacturing machine130 can receive the computer design file from the database 109 of thecompute device 101 and begin three-dimensional object production of thecomponent 140.

At block 220, the system 100 senses in situ physical properties of thecomponent 140 during the material deposition process. In accordance withone or more embodiments, the at least one sensor device 131 is an X-raydetector that acquires an X-ray diffraction (XRD) pattern while thecomponent 140 is in a formation state (prior to completion). Variousparameters of the XRD pattern are analyzed by the software 108 of thecompute device 101 to determine the in situ physical properties ormaterial parameters, such as hardness, local strain, yield strength,density, crystallite size, porosity, defect density and compositionalvariation (among other properties). The XRD pattern can be taken fromany area of interest of the component 140, as directed by the computedevice 101.

At block 230, the system 100 detects compliance to specifications ordefects of the in situ physical properties with respect to pre-specifiedmaterial requirements. In this regard, the compute device 101 cancompare the pre-specified material requirements of the database 109 tothe in situ physical properties and determine if any defects arepresent. At block 240, all defects are analyzed by the system 100 (e.g.,by the software 108 of the compute device 101) to determine whethercorrective actions need to be taken and what those corrective actionshould be.

At block 250, the system 100 feeds forward and back the correctiveactions to the material deposition process in real time for continuousadaptability, thereby updating the material deposition process (e.g.,altering additive manufacturing depositions) to account for the defectsand achieve pre-specified material requirements. At block 260, thesystem 100 implements the material deposition process with thecorrective actions to complete the manufacturing of the component 140.

Turning now to FIG. 3, a schematic flow 300 is depicted according to oneor more embodiments. The schematic flow 300 is an example operation ofimplementing in situ monitoring of stress for a component (including insitu and post situ process controls) by a system. The schematic flow 300is executed by an additive manufacturing machine 301 comprising an X-raysource 302 and an X-ray detector 303 (e.g., an example of the sensordevice 131 of FIG. 1) and a computing device 304. To the extent thatthese items overlap with the above system 100, further description isnot provided for the sake of brevity.

In general, the schematic flow 300 depicts a model 305 and a toolpathplanning being received by the additive manufacturing machine 301 andutilized in a production operation 315 to produce a component. Due toany number of factors during the production operation 315, the additivemanufacturing machine 301 may produce a trending component 320. Thetrending component 320 is note desired as a final component.

As shown in FIG. 3, the computing device 304 executes a sensing phase330 through a process sensing 322. The process sensing 322 includesreceiving physical properties of the component while the component is ina formation state. The X-ray source 302 generates X-rays so that an XRDpattern can be taken from any area of interest by the X-ray detector303. The physical properties are communicated by the X-ray detector 303of the additive manufacturing machine 301, which is performing the insitu monitoring. The process sensing 322 further include comparingpre-specified material requirements to the in situ physical propertiesto provide comparison information. The sensing phase 330 and the processsensing 322 can be implemented by software of the computing device 304.

Next, the computing device 304 executes a detecting phase 340, whichincludes a process defect detection 342, a layer defect detection 344,and a part defect detection 346. The detecting phase 340 identifiesdefects with respect to errors in the process (e.g., the process defectdetection 342), defect within one or more layers (e.g., the layer defectdetection 344), and defects across the component itself (e.g., the partdefect detection 346). The detecting phase 340 and operations thereincan be implemented by software of the computing device 304.

The computing device 304 also executes a reacting phase 350, whichincludes a feedback control 352, a scan path planning 354, and adecision making 356. The reacting phase 350 and operations therein canbe implemented by software of the computing device 304. The results ofthe reacting 350 phase include corrective actions that are provided tothe production operation 315. The corrected actions can includeadjusting an area of interest to determine where to perform the in situmonitoring (e.g., by the feedback control 352), adjusting a scan path toaccommodate or correct defects in the trending component 320 (e.g., bythe scan path planning 354), and determining material deposit amounts toaccommodate or correct defects in the trending component 320 (e.g., bythe decision making 356). The production operation 315 is improved bythe corrective actions from the computing device, such that the additivemanufacturing machine 301 may now produce a desired component 350.

The term “about” is intended to include the degree of error associatedwith measurement of the particular quantity based upon the equipmentavailable at the time of filing the application.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the presentdisclosure. As used herein, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprises” and/or “comprising,” when used in this specification,specify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,element components, and/or groups thereof.

While the present disclosure has been described with reference to anexemplary embodiment or embodiments, it will be understood by thoseskilled in the art that various changes may be made and equivalents maybe substituted for elements thereof without departing from the scope ofthe present disclosure. In addition, many modifications may be made toadapt a particular situation or material to the teachings of the presentdisclosure without departing from the essential scope thereof.Therefore, it is intended that the present disclosure not be limited tothe particular embodiment disclosed as the best mode contemplated forcarrying out this present disclosure, but that the present disclosurewill include all embodiments falling within the scope of the claims.

What is claimed is:
 1. A system for implementing a three-dimensionalobject production of a component via an additive manufacturing, thesystem comprising: an additive manufacturing machine comprising an X-raysource and an X-ray detector; and a compute device comprising aprocessor and a memory, the compute device being communicatively coupledto the additive manufacturing machine and the X-ray source and the X-raydetector, wherein the additive manufacturing machine and the computedevice provide in situ sensor analysis of the component while in aformation state during a material deposition process of the additivemanufacturing by: sensing, by the X-ray source and the X-ray detector,in situ physical properties at an area of interest of the componentduring the three-dimensional object production, the X-ray source and theX-ray detector being axially offset from each other such that the X-raysource and the X-ray detector are not axially aligned with respect tothe area of interest, wherein the in situ physical properties includehardness, local strain, yield strength, crystallite size, defectdensity, crystalline orientation, or texture; detecting compliance tospecifications or defects in the in situ physical properties withrespect to pre-specified material requirements; analyzing the defects todetermine corrective actions; implementing an updated three-dimensionalobject production, which includes the corrective actions, to completethe component.
 2. The system of claim 1, wherein the three-dimensionalobject production of the component is implemented according to acomputer design file.
 3. The system of claim 1, wherein the computedevice feeds forward and back the corrective actions to thethree-dimensional object production in real time to generate the updatedthree-dimensional object production.
 4. The system of claim 1, whereinthe X-ray source and the X-ray detector that together acquire a full orpartial X-ray diffraction signal or pattern that is analyzed todetermine the in situ physical properties.
 5. The system of claim 4,wherein the in situ physical properties additionally include density,porosity, or compositional variation.
 6. The system of claim 1, whereina compute device comprises a processor executing software to provide oneor more process modeling, toolpath planning, defect detection, layerdefect detection, part defect detection, feedback control, scan pathplanning, decision making, and process sensing operations for detectingthe defects.
 7. The system of claim 1, wherein a compute devicecomprises a database storing and providing the pre-specified materialrequirements and a computer design file for detecting the defects andimplementing the three-dimensional object production.